Peking University

Three months after founding, SiMate released Simate-beta, a general physical manipulation system using 4D physical perception and hierarchical temporal memory for zero- and few-shot tasks. The company says it tops the RoboDojo leaderboard with an average score of 33.95 and a 27.96% success rate. Internal testers include researchers from MIT, California Institute of Technology, Tsinghua University and Peking University.

SiMate also disclosed an AutoResearch automation platform and Sinfra infrastructure, and said it raised several consecutive rounds in the hundreds of millions of yuan (tens of millions of dollars). Its founders previously shipped a one-stage end-to-end autonomous driving model to mass production. The company plans phased open-sourcing of the model and research tooling by year-end.

MetaRSI-v1, a meta recursive self-improvement architecture, has been released by CosmosMind jointly with Tsinghua University, Peking University, Stanford University, UC Berkeley, MIT and other institutions. It unifies what the team calls model-level, data-level and harness-level recursion, aiming to improve the process of self-improvement rather than a single model.

With no external teacher model, a 3B active-parameter model (Qwen3.5-35B-A3B) averaged a 10.9-point improvement across Terminal-Bench 2.1, SWE-bench Pro, a GPQA-Diamond subset and AIME; six frontier flagship models averaged a 7.3-point gain on Terminal-Bench 2.1. The RSI-Harness is open-sourced, and the collaboration plans to extend the loop to programmable instruments and automated labs.

At the 2026 China Computing Power Conference, China Mobile Cloud, CETC Nanhu Research Institute, Lynxi Technologies, Iluvatar CoreX, Tsinghua University and Peking University released what they describe as China’s first domestic GPU + neuromorphic chip heterogeneous hybrid LLM inference system. The system splits large-model computation: attention work goes to domestic GPUs while latency-sensitive FFN/MoE expert modules run on neuromorphic chips, coordinated by a self-developed compiler, interconnect protocol and unified inference engine.

In tests with DeepSeek V4, the partners say the setup improves cost-performance more than twofold compared with similar domestic GPU clusters and cuts operating costs by over 40%. It is aimed at token factories, AI code generation, multi-agent collaboration and smart manufacturing.

Jiyuan Lvdong has released NeoHorse-1, its first agent-native model, in 4B and 9B parameter versions. The company says the model applies multi-model execution experience from its Routing Harness system to agent post-training through route-guided curriculum learning.

Infinigence AI provided compute and infrastructure optimization, while Tsinghua University and Peking University contributed to algorithm research. On 10 agent benchmarks, the 4B version achieves state-of-the-art results among models of the same size, according to the release.